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Language models' brittle behavior under distribution shift isn't just about the data you see; unobservable variables are creating an "omitted variable bias" that compromises both evaluation and optimization.
LLMs can reason more causally by simply checking if their counterfactual predictions are consistent, even without any extra training data.
